| 26 Jan 2026
Local weather scenarios for soil and crop models: a simple generator based on historic data sampling
Abstract. Weather scenarios are for example required to model future agricultural production and the development of soil properties under climate change. These scenarios should realistically depict regional weather conditions at a daily resolution for the expected climate development. In this technical note, we present the LocalWeatherSampler (LWS) for generating mid-term weather scenarios (20–30 years) for specific regions or locations based on historically recorded weather data. It is demonstrated for an example site in Germany. The core idea is to define wet or dry years and to increase their abundance in future years via a random sampling from history. A temperature trend based on common climate projections can be added afterwards. For the definition of dry/wet years, two different methods are implemented. The historical weather data can be either divided manually into a pool of wet (or dry) years or based on the Standardized Precipitation Index (SPI). By varying the threshold value for wet (dry) years and their probability of appearance within the scenario, the framework allows for the generation of scenarios tailored to specific requirements, such as sequences characterized by extremely dry years or by moderately dry years, as well as extremely wet future sequences. This approach is designed to test or analyze future scenarios of precipitation regimes and temperature trends using models that require realistic daily weather data, such as soil, crop, or hydrological models.
Reviewing of the manuscript titled ‘Local weather scenarios for soil and crop models: a simple generator based on historic data sampling’ submitted to the discussion of Geoscientific Model Development (Manuscript Number: egusphere-2025-6173). The authors develop a simple generator based on the historic precipitation data for the local weather scenarios (e.g., wet or dry). The time period is long and fine (e.g., 1993-2022 or historic weather and 2023-2052 for projections). The manuscript is overly simple and written in a study that is not strict and not including sufficient literature review and discussion. In my opinion, this is a simple formulation using the R statistics for the data analysis and case study, which lacks strong novelty to fill the research gaps from previous studies. There are also a couple of specific comments. For example, (1) in Figure 1, the reviewer is confused that why in step 4 for Future Scenarios the selected years still 2001-2021? (2) the paragraph should be line well instead of randomly being located, such as in Section 2.1; (3) Is there any statistics to evaluate the validity and accuracy of the proposed model or method? The authors need substantial works to include more details in each section.